Cut LLM Costs: Orchestrate Claude's Batch API with Spring Batch

Developers can reduce costs and improve resource efficiency by using Spring Batch 5.x and Java 21+ Virtual Threads to manage Anthropic’s Claude 3.5 Sonnet Batch API. The author argues that directly calling Claude’s API for offline tasks like data labeling will become prohibitively expensive in 2026.

Traditional methods of handling asynchronous API calls, like spawning threads or custom polling, are inefficient and fragile. The proposed solution leverages Spring Batch’s chunk processing and Virtual Threads for non-blocking polling of the Claude Batch API.

By persisting batch job IDs in the Spring Batch metadata database, the system can resume after failures. This approach offers a 50% cost reduction for non-real-time LLM requests, improved resource efficiency, and enterprise-level reliability for managing AI workflows. The author provides code examples and links to javalld.com for further exploration.